Impact Of A Medical Student Led Walk With A Future Doc Program On Executive Function In Older Adults
Bibliographic record
Abstract
With advancing age is an associated decline in executive function that predisposes older adults to cognitive disorders. Community exercise programs are an effective model for promoting more physical activity to older adults in the local area. Walk with a Future Doc is a model that is feasible and increases members’ movement, but whether this translates to improvements in executive function is unclear. PURPOSE: To test the hypothesis that a 12-week Walk with a Future Doc education and low-impact walking program improves reaction time on a Trail-Making-Task. METHODS: From 2022-2023, we implemented a Walk with a Future Doc program in which medical students discuss health topics with community members followed by 50 minutes of a self-paced walk. The group met weekly for one hour for up to 12 weeks. Participants completed the Trail Making Test at intake and at program completion. Time to do and the errors doing Trail A (1-2-3, etc.; processing speed) and Trail B (1-A-2-B, etc.; cognitive flexibility) were determined. Reaction time and number of errors were compared pre-post (via paired t-tests or Wilcoxon-signed rank tests) RESULTS: We recruited 27 older adults from the community (aged: 63 ± 7 years, 70% female, 59% participants: >3 chronic health problems and conditions, 33% participants: no chronic health problems). Trail A reaction time was faster following the program (29.3 ± 10.7 s to 25.2 ± 6.5 s; p < 0.001) and the number of errors during Trail A decreased (0.4 ± 0.7 to 0.1 ± 0.4; p = 0.02). However, there was no change in the reaction time (60.9 ± 20.6 s to 57.2 ± 18.1 s; p = 0.16) or errors (0.5 ± 1.1 versus 0.2 ± 0.6; p = 0.11) during Trail B from Baseline to Follow-Up. CONCLUSIONS: Our 12-week Walk with a Future Doc program demonstrated a beneficial improvement on the lower-order cognitive processes of older adults, as evident by the improvement in Trail A reaction time (processing speed). A larger sample size may be required to observe a statistically significant improvement in executive function. Community programs may be useful for promoting healthy cognitive aging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".